Deep learning acceleration of iterative model-based light fluence correction for photoacoustic tomography

Liang, Zhaoyong; Zhang, Shuangyang; Liang, Zhichao; Mo, Zongxin; Zhang, Xiaoming; Zhong, Yutian; Chen, Wufan; Qi, Li

doi:10.1016/j.pacs.2024.100601

Cited by 1 publication

(1 citation statement)

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“…With the continuous development of medical imaging technology, photoacoustic imaging (PAI) has attracted more and more attention as a fast-developing hybrid biomedical imaging technology [1][2][3]. PAI combines the advantages of optical imaging and ultrasound imaging, and has unique advantages in imaging depth, spatial resolution, and tissue imaging.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images

Jiang,

He,

Chen

et al. 2024

Applied Sciences

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Photoacoustic imaging (PAI) is an emerging imaging technique that offers real-time, non-invasive, and radiation-free measurements of optical tissue properties. However, image quality degradation due to factors such as non-ideal signal detection hampers its clinical applicability. To address this challenge, this paper proposes an algorithm for super-resolution reconstruction and segmentation based on deep learning. The proposed enhanced deep super-resolution minimalistic network (EDSR-M) not only mitigates the shortcomings of the original algorithm regarding computational complexity and parameter count but also employs residual learning and attention mechanisms to extract image features and enhance image details, thereby achieving high-quality reconstruction of PAI. DeepLabV3+ is used to segment the images before and after reconstruction to verify the network reconstruction performance. The experimental results demonstrate average improvements of 19.76% in peak-signal-to-noise ratio (PSNR) and 4.80% in structural similarity index (SSIM) for the reconstructed images compared to those of their pre-reconstructed counterparts. Additionally, mean accuracy, mean intersection and union ratio (IoU), and mean boundary F1 score (BFScore) for segmentation showed enhancements of 8.27%, 6.20%, and 6.28%, respectively. The proposed algorithm enhances the effect and texture features of PAI and makes the overall structure of the image restoration more complete.

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Section: Introductionmentioning

confidence: 99%